This book provides a clear, modern, and application‑driven introduction to mathematical optimization—one of the central technologies underlying today’s data‑driven decision making. Designed for students and practitioners encountering optimization for the first time, the book develops core concepts through intuitive explanations, concrete examples, and algorithmic insights, while maintaining mathematical rigor where it matters.Beginning with smooth unconstrained problems, the text builds a unified framework connecting calculus, convexity, and optimality conditions. It progresses naturally to constrained and nonsmooth optimization, introducing subgradients, normal cones, and the Karush–Kuhn–Tucker condition in an accessible and coherent manner. Along the way, readers learn how fundamental algorithms—such as gradient, Newton’s, and simplex methods—emerge from the underlying theory and how they behave in practice.The book places strong emphasis on modeling, guiding readers from real‑world problems to optimization formulations and solution strategies. Topics include linear and quadratic programming, network and integer structures, and optimization under uncertainty. Applications are drawn from engineering, operations research, data science, machine learning, and analytics, illustrating how optimization serves as a unifying language across disciplines.Written as lecture notes for a one‑semester introductory course, this book assumes only basic knowledge of calculus and linear algebra. It builds intuition, vocabulary, and mathematical comprehension, while largely avoiding formal proofs. Numerous worked examples, exercises, and fully solved problems support self‑study and classroom use.This book is ideal for advanced undergraduate students, master’s students, and practitioners seeking a principled yet approachable introduction to optimization, as well as a foundation for further study in applied mathematics, operations research, and analytics.